mars

mars provides tools for meta-analysis and research synthesis in R, with support for univariate, multivariate, and multilevel meta-analytic models. It also includes helpers for within-study variance-covariance matrices, path analysis after correlation synthesis, publication-bias models, diagnostics, network meta-analysis, reporting, and exploratory random-forest meta-analysis.

Installation

Install the released version from CRAN with:

install.packages("mars")

The package can be installed directly from GitHub using devtools or the remotes package with the following command:

remotes::install_github("lebebr01/mars")

To install the package vignettes, add the build_vignettes argument:

remotes::install_github("lebebr01/mars",
                        build_vignettes = TRUE)

Quick Example

A univariate random-effects meta-analysis can be fit with mars() by supplying an effect-size column, a sampling-variance column, and a study identifier:

library(mars)

fit <- mars(
  data = teacher_expectancy,
  studyID = "study",
  effectID = NULL,
  sample_size = NULL,
  formula = yi ~ 1,
  variance = "vi",
  varcov_type = "univariate",
  structure = "univariate"
)

summary(fit)

Explore articles

The installed package includes core vignettes. Additional workflow articles are built for the pkgdown site.

Available installed vignettes can be accessed with the following code:

vignette(package = 'mars')